How to improve the quality of conversations in online communities has attracted considerable attention recently. Having engaged, urbane, and reactive online conversations has a critical effect on the social life of Internet users. In this study, we are particularly interested in identifying a post in a multi-party conversation that is unlikely to be further replied to, which therefore kills that thread of the conversation. For this purpose, we propose a deep learning model called the ConverNet. ConverNet is attractive due to its capability of modeling the internal structure of a long conversation and its appropriate encoding of the contextual information of the conversation, through effective integration of attention mechanisms. Empirical experiments on real-world datasets demonstrate the effectiveness of the proposal model. For the widely concerned topic, our analysis also offers implications for improving the quality and user experience of online conversations.
@article{arxiv.1712.08636,
title = {Find the Conversation Killers: a Predictive Study of Thread-ending Posts},
author = {Yunhao Jiao and Cheng Li and Fei Wu and Qiaozhu Mei},
journal= {arXiv preprint arXiv:1712.08636},
year = {2017}
}